{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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DPTNAMEADDRESSTTLPCSAL-RATE
0868B J SANDIFORDDEPARTMENT OF CITYWIDE ADM12702X$5.00
1868C A WIGFALLDEPARTMENT OF CITYWIDE ADM12702X$5.00
269A E A-AWOSOGBAHRA/DEPARTMENT OF SOCIAL S52311A$51955.00
3868K D AABYDEPARTMENT OF CITYWIDE ADM10209X$12.00
456I D AADILPOLICE DEPARTMENT71012A$46953.00
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" ], "text/plain": [ " DPT NAME ADDRESS TTL PC SAL-RATE\n", "0 868 B J SANDIFORD DEPARTMENT OF CITYWIDE ADM 12702 X $5.00\n", "1 868 C A WIGFALL DEPARTMENT OF CITYWIDE ADM 12702 X $5.00\n", "2 69 A E A-AWOSOGBA HRA/DEPARTMENT OF SOCIAL S 52311 A $51955.00\n", "3 868 K D AABY DEPARTMENT OF CITYWIDE ADM 10209 X $12.00\n", "4 56 I D AADIL POLICE DEPARTMENT 71012 A $46953.00" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"Civil_List_2014.csv\", \n", " names=[\"DPT\",\"NAME\",\"ADDRESS\",\"TTL\", \"PC\",\"SAL-RATE\"], \n", " skiprows=1,\n", " dtype=col_types)\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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DPTNAMEADDRESSTTLPCSAL-RATE
0DPTNAMEADDRESSTTL #PCSAL-RATE
1868B J SANDIFORDDEPARTMENT OF CITYWIDE ADM12702X$5.00
2868C A WIGFALLDEPARTMENT OF CITYWIDE ADM12702X$5.00
369A E A-AWOSOGBAHRA/DEPARTMENT OF SOCIAL S52311A$51955.00
4868K D AABYDEPARTMENT OF CITYWIDE ADM10209X$12.00
556I D AADILPOLICE DEPARTMENT71012A$46953.00
669M AAKIRIHRA/DEPARTMENT OF SOCIAL S56056A$33000.00
7464A AALAICUNY QUEENSBOROUGH COMMUNI4607N$73.53
8998A V AALEVIKN.Y.C. TRANSIT AUTHORITY4022$33280.00
9998M AAMIRN.Y.C. TRANSIT AUTHORITY00T074$60878.00
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" ], "text/plain": [ " DPT NAME ADDRESS TTL PC \\\n", "0 DPT NAME ADDRESS TTL # PC \n", "1 868 B J SANDIFORD DEPARTMENT OF CITYWIDE ADM 12702 X \n", "2 868 C A WIGFALL DEPARTMENT OF CITYWIDE ADM 12702 X \n", "3 69 A E A-AWOSOGBA HRA/DEPARTMENT OF SOCIAL S 52311 A \n", "4 868 K D AABY DEPARTMENT OF CITYWIDE ADM 10209 X \n", "5 56 I D AADIL POLICE DEPARTMENT 71012 A \n", "6 69 M AAKIRI HRA/DEPARTMENT OF SOCIAL S 56056 A \n", "7 464 A AALAI CUNY QUEENSBOROUGH COMMUNI 4607 N \n", "8 998 A V AALEVIK N.Y.C. TRANSIT AUTHORITY 402 2 \n", "9 998 M AAMIR N.Y.C. TRANSIT AUTHORITY 00T07 4 \n", "\n", " SAL-RATE \n", "0 SAL-RATE \n", "1 $5.00 \n", "2 $5.00 \n", "3 $51955.00 \n", "4 $12.00 \n", "5 $46953.00 \n", "6 $33000.00 \n", "7 $73.53 \n", "8 $33280.00 \n", "9 $60878.00 " ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(10)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 269838\n", "unique 27533\n", "top $76488.00\n", "freq 26024\n", "Name: SAL-RATE, dtype: object" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['SAL-RATE'].describe()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "ename": "KeyError", "evalue": "'PC'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 1944\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1945\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1946\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4154)\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4018)\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12368)\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12322)\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'PC'", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'PC'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1995\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1996\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1997\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1998\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1999\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2002\u001b[0m \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2003\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2004\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2006\u001b[0m \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1348\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1349\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1350\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1351\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1352\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m 3288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3289\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3290\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3291\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3292\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 1945\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1946\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1947\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1948\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1949\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4154)\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4018)\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12368)\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12322)\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'PC'" ] } ], "source": [ "df['PC'].value_counts()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['DPT ', 'NAME ', 'ADDRESS ', 'TTL # ', 'PC ', 'SAL-RATE'], dtype='object')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'DPT'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"DPT \".strip()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DPT\n", "NAME\n", "ADDRESS\n", "TTL #\n", "PC\n", "SAL-RATE\n" ] } ], "source": [ "for col in df.columns:\n", " print(col.strip())" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['DPT', 'NAME', 'ADDRESS', 'TTL #', 'PC', 'SAL-RATE']" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# it's a list, so - []\n", "# it's a loop, so - [for col in df.columns]\n", "# col.strip(), so - [col.strip() for col in columns]\n", "\n", "[col.strip() for col in df.columns]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.columns = [col.strip() for col in df.columns]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['DPT', 'NAME', 'ADDRESS', 'TTL #', 'PC', 'SAL-RATE'], dtype='object')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "A 100521\n", "D 54881\n", "X 24403\n", "4 23259\n", "3 19866\n", "1 13083\n", "K 7635\n", "F 6361\n", "N 4671\n", "U 3409\n", "B 2817\n", "BW 1766\n", "E 1300\n", "W 1257\n", "G 1085\n", "I 730\n", "2 721\n", "M 708\n", "O 536\n", "WK 488\n", "P 307\n", "L 25\n", "S 4\n", "0 3\n", "V 2\n", "Name: PC, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['PC'].value_counts()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 269838\n", "unique 27533\n", "top $76488.00\n", "freq 26024\n", "Name: SAL-RATE, dtype: object" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['SAL-RATE'].describe()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "76488.0\n", "5.0\n", "1500.0\n" ] } ], "source": [ "def money_to_float(money_str):\n", " return float(money_str.replace(\"$\",\"\").replace(\",\",\"\"))\n", "\n", "print(money_to_int(\"$76488.00\"))\n", "print(money_to_int(\"$5.00\"))\n", "print(money_to_int(\"$1,500.00\"))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 5.00\n", "1 5.00\n", "2 51955.00\n", "3 12.00\n", "4 46953.00\n", "5 33000.00\n", "6 73.53\n", "7 33280.00\n", "8 60878.00\n", "9 69272.00\n", "10 89960.00\n", "11 38324.00\n", "12 39019.00\n", "13 369.92\n", "14 66127.00\n", "15 64250.00\n", "16 32510.00\n", "17 2.65\n", "18 109087.00\n", "19 111606.00\n", "20 5.00\n", "21 56555.00\n", "22 51643.00\n", "23 77015.00\n", "24 76488.00\n", "25 67627.00\n", "26 68095.00\n", "27 66127.00\n", "28 76689.00\n", "29 48153.00\n", " ... \n", "269808 85287.00\n", "269809 65564.00\n", "269810 56937.00\n", "269811 81439.00\n", "269812 118506.00\n", "269813 42064.00\n", "269814 33.18\n", "269815 116364.00\n", "269816 98072.00\n", "269817 107563.00\n", "269818 37150.00\n", "269819 74646.00\n", "269820 64040.00\n", "269821 76488.00\n", "269822 83802.00\n", "269823 17.16\n", "269824 85667.00\n", "269825 46.89\n", "269826 67.42\n", "269827 51916.00\n", "269828 57514.00\n", "269829 74605.00\n", "269830 74605.00\n", "269831 64403.00\n", "269832 51936.00\n", "269833 47.58\n", "269834 87738.00\n", "269835 78683.00\n", "269836 76488.00\n", "269837 5.00\n", "Name: SAL-RATE, dtype: float64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#money_to_float(df['SAL-RATE'])\n", "df['SAL-RATE'].apply(money_to_float)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['salary'] = df['SAL-RATE'].apply(money_to_float)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { 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DPTNAMEADDRESSTTL #PCSAL-RATEsalary
0868B J SANDIFORDDEPARTMENT OF CITYWIDE ADM12702X$5.005.0
1868C A WIGFALLDEPARTMENT OF CITYWIDE ADM12702X$5.005.0
269A E A-AWOSOGBAHRA/DEPARTMENT OF SOCIAL S52311A$51955.0051955.0
3868K D AABYDEPARTMENT OF CITYWIDE ADM10209X$12.0012.0
456I D AADILPOLICE DEPARTMENT71012A$46953.0046953.0
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" ], "text/plain": [ " DPT NAME ADDRESS TTL # PC SAL-RATE \\\n", "0 868 B J SANDIFORD DEPARTMENT OF CITYWIDE ADM 12702 X $5.00 \n", "1 868 C A WIGFALL DEPARTMENT OF CITYWIDE ADM 12702 X $5.00 \n", "2 69 A E A-AWOSOGBA HRA/DEPARTMENT OF SOCIAL S 52311 A $51955.00 \n", "3 868 K D AABY DEPARTMENT OF CITYWIDE ADM 10209 X $12.00 \n", "4 56 I D AADIL POLICE DEPARTMENT 71012 A $46953.00 \n", "\n", " salary \n", "0 5.0 \n", "1 5.0 \n", "2 51955.0 \n", "3 12.0 \n", "4 46953.0 " ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 269838.000000\n", "mean 54143.707486\n", "std 32345.656289\n", "min 0.500000\n", "25% 36899.000000\n", "50% 57855.000000\n", "75% 76488.000000\n", "max 300000.000000\n", "Name: salary, dtype: float64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['salary'].describe()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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DPTNAMEADDRESSTTL #PCSAL-RATEsalary
0868B J SANDIFORDDEPARTMENT OF CITYWIDE ADM12702X$5.005.0
1868C A WIGFALLDEPARTMENT OF CITYWIDE ADM12702X$5.005.0
269A E A-AWOSOGBAHRA/DEPARTMENT OF SOCIAL S52311A$51955.0051955.0
3868K D AABYDEPARTMENT OF CITYWIDE ADM10209X$12.0012.0
456I D AADILPOLICE DEPARTMENT71012A$46953.0046953.0
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" ], "text/plain": [ " DPT NAME ADDRESS TTL # PC SAL-RATE \\\n", "0 868 B J SANDIFORD DEPARTMENT OF CITYWIDE ADM 12702 X $5.00 \n", "1 868 C A WIGFALL DEPARTMENT OF CITYWIDE ADM 12702 X $5.00 \n", "2 69 A E A-AWOSOGBA HRA/DEPARTMENT OF SOCIAL S 52311 A $51955.00 \n", "3 868 K D AABY DEPARTMENT OF CITYWIDE ADM 10209 X $12.00 \n", "4 56 I D AADIL POLICE DEPARTMENT 71012 A $46953.00 \n", "\n", " salary \n", "0 5.0 \n", "1 5.0 \n", "2 51955.0 \n", "3 12.0 \n", "4 46953.0 " ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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codeagencyis_mayoral
0002OFFICE OF THE MAYORN
1003BOARD OF ELECTIONSY
2004CAMPAIGN FINANCE BOARDY
3005BOARD OF ESTIMATE-SECRETARYY
4006MAYOR'S OFFICE OF OPERATIONSN
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" ], "text/plain": [ " code agency is_mayoral\n", "0 002 OFFICE OF THE MAYOR N\n", "1 003 BOARD OF ELECTIONS Y\n", "2 004 CAMPAIGN FINANCE BOARD Y\n", "3 005 BOARD OF ESTIMATE-SECRETARY Y\n", "4 006 MAYOR'S OFFICE OF OPERATIONS N" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "agencies_df = pd.read_csv(\"cleaned-agencies.csv\", dtype='str')\n", "agencies_df.head()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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codeagencyis_mayoral
0002OFFICE OF THE MAYORN
1003BOARD OF ELECTIONSY
2004CAMPAIGN FINANCE BOARDY
3005BOARD OF ESTIMATE-SECRETARYY
4006MAYOR'S OFFICE OF OPERATIONSN
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" ], "text/plain": [ " code agency is_mayoral\n", "0 002 OFFICE OF THE MAYOR N\n", "1 003 BOARD OF ELECTIONS Y\n", "2 004 CAMPAIGN FINANCE BOARD Y\n", "3 005 BOARD OF ESTIMATE-SECRETARY Y\n", "4 006 MAYOR'S OFFICE OF OPERATIONS N" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "col_types = { 'code': 'str' }\n", "agencies_df = pd.read_csv(\"cleaned-agencies.csv\", dtype=col_types)\n", "agencies_df.head()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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codeagencyis_mayoral
0-999THIS IS FAKEFalse
1-999THIS IS ALSO FAKETrue
2-999THESE ARE ALL FAKEFalse
3002OFFICE OF THE MAYORFalse
4003BOARD OF ELECTIONSTrue
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" ], "text/plain": [ " code agency is_mayoral\n", "0 -999 THIS IS FAKE False\n", "1 -999 THIS IS ALSO FAKE True\n", "2 -999 THESE ARE ALL FAKE False\n", "3 002 OFFICE OF THE MAYOR False\n", "4 003 BOARD OF ELECTIONS True" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "col_types = { 'code': 'str' }\n", "agencies_df = pd.read_csv(\"cleaned-agencies.csv\", dtype=col_types, true_values='Y', false_values='N')\n", "agencies_df.head()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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codeagencyis_mayoral
0NaNTHIS IS FAKEFalse
1NaNTHIS IS ALSO FAKETrue
2NaNTHESE ARE ALL FAKEFalse
3002OFFICE OF THE MAYORFalse
4003BOARD OF ELECTIONSTrue
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" ], "text/plain": [ " code agency is_mayoral\n", "0 NaN THIS IS FAKE False\n", "1 NaN THIS IS ALSO FAKE True\n", "2 NaN THESE ARE ALL FAKE False\n", "3 002 OFFICE OF THE MAYOR False\n", "4 003 BOARD OF ELECTIONS True" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "col_types = { 'code': 'str' }\n", "agencies_df = pd.read_csv(\"cleaned-agencies.csv\", na_values=[\"-999\"], dtype=col_types, true_values='Y', false_values='N')\n", "agencies_df.head()" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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codeagencyis_mayoral
0-999THIS IS FAKEN
1-999THIS IS ALSO FAKEY
2-999THESE ARE ALL FAKEN
32OFFICE OF THE MAYORN
43BOARD OF ELECTIONSY
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" ], "text/plain": [ " code agency is_mayoral\n", "0 -999 THIS IS FAKE N\n", "1 -999 THIS IS ALSO FAKE Y\n", "2 -999 THESE ARE ALL FAKE N\n", "3 2 OFFICE OF THE MAYOR N\n", "4 3 BOARD OF ELECTIONS Y" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "agencies_df = pd.read_csv(\"cleaned-agencies.csv\")\n", "agencies_df.head()" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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DPTNAMEADDRESSTTLPCSAL-RATE
0868B J SANDIFORDDEPARTMENT OF CITYWIDE ADM12702X$5.00
1868C A WIGFALLDEPARTMENT OF CITYWIDE ADM12702X$5.00
269A E A-AWOSOGBAHRA/DEPARTMENT OF SOCIAL S52311A$51955.00
3868K D AABYDEPARTMENT OF CITYWIDE ADM10209X$12.00
456I D AADILPOLICE DEPARTMENT71012A$46953.00
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" ], "text/plain": [ " DPT NAME ADDRESS TTL PC SAL-RATE\n", "0 868 B J SANDIFORD DEPARTMENT OF CITYWIDE ADM 12702 X $5.00\n", "1 868 C A WIGFALL DEPARTMENT OF CITYWIDE ADM 12702 X $5.00\n", "2 69 A E A-AWOSOGBA HRA/DEPARTMENT OF SOCIAL S 52311 A $51955.00\n", "3 868 K D AABY DEPARTMENT OF CITYWIDE ADM 10209 X $12.00\n", "4 56 I D AADIL POLICE DEPARTMENT 71012 A $46953.00" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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DPTNAMEADDRESSTTLPCSAL-RATEcodeagencyis_mayoral
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [DPT, NAME, ADDRESS, TTL, PC, SAL-RATE, code, agency, is_mayoral]\n", "Index: []" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.merge(agencies_df, left_on='DPT', right_on='code')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }